Reported improvements are to a large extent due to the availability of large datasets [1]}, [2]}, [3]}, computational performance advances, e.g., for GPUs, and a better understanding about how to encode inductive biases into deep-nets, e.g., by using rectified linear units [4]}, normalization [5]}, skip connections [6]}, transformers [7]}, etc. However, importantly, developed deep-net architectures are not guaranteed to solve a given task. There is a chance that they may instead exploit dataset biases.
